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This research study focuses on the application models of Data Mining and Machine Learning covering cellular network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms have been applied on real cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: RStudio for Machine Learning and process visualization, Apache Spark, SparkSQL for data and big data processing and clicData for service Visualization. Two use cases have been studied during this research. In the first study, the process of Data and predictive Analytics are fully applied in the field of Telecommunications to efficiently address users’ experience, in the goal of increasing customer loyalty and decreasing churn or customer attrition. Using real cellular network transactions, prediction analytics are used to predict customers who are likely to churn, which can result in revenue loss. Prediction algorithms and models including Classification Tree, Random Forest, Neural Networks and Gradient boosting have been used with an exploratory Data Analysis, determining relationship between predicting variables. The data is segmented in to two, a training set to train the model and a testing set to test the model. The evaluation of the best performing model is based on the prediction accuracy, sensitivity, specificity and the Confusion Matrix on the test set. The second use case analyses Service Quality Management using modern data mining techniques and the advantages of in-memory big data processing with Apache Spark and SparkSQL to save cost on tool investment; thus, a low-cost Service Quality Management model is proposed and analyzed. With increase in Smart phone adoption, access to mobile internet services, applications such as streaming, interactive chats require a certain service level to ensure customer satisfaction. As a result, an SQM framework is developed with Service Quality Index (SQI) and Key Performance Index (KPI). The research concludes with recommendations and future studies around modern technology applications in Telecommunications including Internet of Things (IoT), Cloud and recommender systems. / Cellular networks have evolved and are still evolving, from traditional GSM (Global System for Mobile Communication) Circuit switched which only supported voice services and extremely low data rate, to LTE all Packet networks accommodating high speed data used for various service applications such as video streaming, video conferencing, heavy torrent download; and for say in a near future the roll-out of the Fifth generation (5G) cellular networks, intended to support complex technologies such as IoT (Internet of Things), High Definition video streaming and projected to cater massive amount of data. With high demand on network services and easy access to mobile phones, billions of transactions are performed by subscribers. The transactions appear in the form of SMSs, Handovers, voice calls, web browsing activities, video and audio streaming, heavy downloads and uploads. Nevertheless, the stormy growth in data traffic and the high requirements of new services introduce bigger challenges to Mobile Network Operators (NMOs) in analysing the big data traffic flowing in the network. Therefore, Quality of Service (QoS) and Quality of Experience (QoE) turn in to a challenge. Inefficiency in mining, analysing data and applying predictive intelligence on network traffic can produce high rate of unhappy customers or subscribers, loss on revenue and negative services’ perspective. Researchers and Service Providers are investing in Data mining, Machine Learning and AI (Artificial Intelligence) methods to manage services and experience. This research study focuses on the application models of Data Mining and Machine Learning covering network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms will be applied on cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: R-Studio for Machine Learning, Apache Spark, SparkSQL for data processing and clicData for Visualization. / Electrical and Mining Engineering / M. Tech (Electrical Engineering)

O presente trabalho aborda a aplicação de códigos fontanais em redes subaquáticas. Tais redes transmitem dados abaixo da água fazendo uso de sinais acústicos e possuem diversas aplicações. No entanto, é sabido que esse tipo de rede é caracterizado por uma baixa velocidade de propagação e largura de banda menor que as redes que operam em meios de transmissão mais conhecidos, tais como a transmissão sem fio via ondas de rádio frequência, resultando num maior atraso na entrega de pacotes. Para tentar minimizar estes atrasos e aumentar a eficiência energética das redes subaquáticas, o trabalho otimizou o sistema de transmissão inserindo um código corretor de erros fontanal no transmissor de mensagens. Dentro desse contexto, foi necessário modelar o consumo energético necessário para a transmissão correta de pacotes de dados em redes subaquáticas utilizando códigos fontanais. Dentre os resultados do trabalho, o mais relevante conclui que o uso dos códigos fontanais é capaz de reduzir em até 30% o consumo de energia quando a distância de transmissão é de 20 km para o caso com a taxa de erro de quadro alvo (FER) de Po = 10^−5, e em ate 25% para a FER alvo de Po = 10^−3. / The present work employs fountain codes in an underwater network, in which data is transmitted using acoustic signals and has many applications. However, underwater networks are usually characterized by low propagation speed and smaller bandwidth than networks that use radio frequency signals, resulting in larger transmission delays. Then, aiming at minimizing the delays and increasing the energy efficiency of underwater networks, the present work employs fountain error-correcting codes at the transmitter. To that end, it was first necessary to model the energy consumption of a success data packet transmission in an underwater network using fountain codes. Our results show that the use of fountain codes is able to reduce up to 30% of energy consumption when the transmission distance is of 20 km for the case with a target frame error rate (FER) of Po = 10^−5 , and 25% for the same distance with a target FER of Po = 10^−3.

Road transport mobility continues to be a challenge to the City of Johannesburg (CoJ)’s economy in general. Traffic signals, their remote monitoring and control systems are the current implemented Intelligent Transport Systems (ITS), but daily systematic faults and maintenance uncertainties on such systems decrease the effectiveness of traffic engineers’ intersections optimization techniques. Inefficient electrical power supply to such ITS is a challenge, with conditional power cuts and fluctuations, uncertainties on traffic control system faults. Another factor leading to the problem is the communication channel which is using traditional modems which are not reliable. Reporting through both customer complaints and such unreliable remote monitoring systems makes maintenance to be ineffective. In this dissertation, the factors leading to the faults and uncertainties are considered. The proposed solution considers the important concerns of ITS, such as electrical power source performance optimization technique, road traffic control systems compatibility and communications systems / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)

In this thesis we provide a model for a bufferless optical burst switching (OBS) and an optical packet switching (OPS) network. The thesis is divided in three parts. <p><p>In the first part we introduce the basic functionality and structure of OBS and OPS networks. We identify the blocking probability as the main performance parameter of interest. <p><p>In the second part we study the statistical properties of the traffic that will likely run through these networks. We use for this purpose a set of traffic traces obtained from the Universidad Politécnica de Catalunya. Our conclusion is that traffic entering the optical domain in future OBS/OPS networks will be long-range dependent (LRD). <p><p>In the third part we present the model for bufferless OBS/OPS networks. This model takes into account the results from the second part of the thesis concerning the LRD nature of traffic. It also takes into account specific issues concerning the functionality of a typical bufferless packet-switching network. The resulting model presents scalability problems, so we propose an approximative method to compute the blocking probability from it. We empirically evaluate the accuracy of this method, as well as its scalability. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished